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ai-system-design-guide

Guide production AI engineers in designing RAG and agent architectures.

3|Updated May 16, 2026
Search Tags:#evaluation#mcp#agentic ai#ai system design#rag architecture#multi-tenant security#llm engineering
One-click install
npx skills add https://github.com/Aradotso/design-skills --skill ai-system-design-guide
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-system-design-guide
Source: https://github.com/Aradotso/design-skills/tree/main/skills/ai-system-design-guide
Command: npx skills add https://github.com/Aradotso/design-skills --skill ai-system-design-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of designing production-grade AI systems by consolidating practical architecture patterns, RAG/agent engineering guidance, and staff-level interview preparation into one continually updated reference.

Core Features & Use Cases

  • Production AI system design: end-to-end guidance for building reliable AI services, including multi-tenant isolation, security, and reliability patterns.
  • RAG architecture engineering: covers chunking, vector retrieval, reranking, contextual retrieval, and late-interaction approaches like ColBERT.
  • Agentic and tool-use workflows: explains MCP-enabled agent design, tool-use orchestration, and computer agents with safety considerations.
  • Evaluation and observability: provides guidance on eval pipelines, monitoring, and quality gates for continuous improvement.

Quick Start

Ask the AI assistant to create a staff-level architecture plan for a multi-tenant RAG system with eval and monitoring, including the retrieval strategy, security boundaries, and a concrete implementation checklist.

Frequently Asked Questions about ai-system-design-guide

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a multi-tenant RAG architecture with proper security boundaries?▼

Designing a multi-tenant RAG architecture requires implementing strict tenant isolation, vector retrieval strategies like chunking and reranking, and robust security boundaries to protect production AI services.

What is the best way to evaluate and monitor LLM agents in production?▼

Evaluating production LLM agents requires establishing comprehensive eval pipelines, continuous observability monitoring, and quality gates to ensure reliable agentic and tool-use workflows.

How does MCP-enabled agent design work for tool-use orchestration?▼

MCP-enabled agent design orchestrates tool-use workflows by integrating computer agents with specific safety considerations, enabling structured interaction between large language models and external tools.

What retrieval patterns should I use for production AI system design?▼

Production AI system design utilizes retrieval patterns including contextual retrieval, late-interaction approaches like ColBERT, vector retrieval, and reranking to optimize RAG architecture performance.

Can I use this guide to prepare for staff-level AI engineering interviews?▼

Yes, this guide consolidates staff-level interview preparation for AI engineering roles by covering production system design, RAG architecture, multi-tenant security, and observability practices.

What are the key reliability requirements for production AI services?▼

Key reliability requirements for production AI services include multi-tenant isolation, continuous evaluation pipelines, observability monitoring, and stringent security patterns to maintain system integrity.

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